Next-Generation NMR Crystallography Through Ab Initio Structure Refinement

University Of California-Riverside, Riverside CA

Investigators

Abstract

Gregory Beran of the University of California Riverside is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Division to develop new computational tools that will facilitate the determination of three-dimensional crystal structures via nuclear magnetic resonance (NMR) spectroscopy. Knowledge of molecular crystal structures is essential in pharmaceuticals and many other areas of chemistry. Different crystal packing motifs ("polymorphs") of the same molecule can exhibit vastly different properties, and occurrences of undesirable polymorphs have caused major drug recalls and other serious problems for patients and pharmaceutical manufacturers. NMR spectroscopy is increasingly used to determine crystal structures and characterize different polymorphs. Translating the set of peaks present in an NMR spectrum into a three-dimensional crystal structure frequently involves trial-and-error computational modeling of potential structures to identify the structure whose predicted NMR spectrum best matches the experimentally observed one. This project develops computational tools to circumvent this trial-and-error process and enable direct crystal structure refinement which can automatically solve for the crystal structure that produces the experimentally observed NMR spectrum. These new tools will significantly improve the utility of NMR for solving crystal structures. Achieving these goals requires research advances in three areas. First, new computationally practical electronic structure methods for modeling the non-covalent interactions that govern crystal packing are being developed to enable identification of good initial crystal structures. Second, because accurate predictions of the NMR chemical shifts for a given structure are essential to discriminating between correct and incorrect structural assignments when comparing predicted and observed spectra, models which predict NMR chemical shifts more reliably than widely used existing techniques are being developed. Third, to enable direct NMR-driven crystal structure refinement, machine learning models that reproduce how changes in molecular conformation and crystal packing impact the chemical shifts are being developed. Success will lead to greater ability to engineer molecular crystals with desired properties. In addition, this award is supporting outreach activities at a local elementary school and production of online tools to educate new users about computational chemistry approaches.